High-Entropy Energy Materials in the Age of Big Data: A Critical Guide to Next-Generation Synthesis and Applications

High-entropy materials (HEMs) with promising energy storage and conversion properties have recently attracted worldwide increasing research interest. Nevertheless, most research on the synthesis of HEMs focuses on a “trial and error” method without any guidance, which is very laborious and time-consuming. This review aims to provide an instructive approach to searching and developing new high-entropy energy materials in a much more efficient way. Toward materials design for future technologies, a fundamental understanding of the process/structure/property/performance linkage on an atomistic level will promote prescreening and selection of material candidates. With the help of computational material science, in which the fast development of computational capabilities that have a rapidly growing impact on new materials design, this fundamental understanding can be approached. Furthermore, high-throughput experimental methods, enabled by the advances in instrumentation and electronics, will accelerate the production of large quantities of results and stimulate the identification of the target products, adding knowledge in computational design. This review shows that combining computational preselection and verification by high-throughput can be an efficient approach to unveil the complexities of HEMs and design novel HEMs with enhanced properties for energy-related applications

High‐Entropy Energy Materials in the Age of Big Data: A Critical Guide to Next‐Generation Synthesis and Applications

High-entropy materials (HEMs) with promising energy storage and conversion properties have recently attracted worldwide increasing research interest. Nevertheless, most research on the synthesis of HEMs focuses on a “trial and error” method without any guidance, which is very laborious and time-consuming. This review aims to provide an instructive approach to searching and developing new high-entropy energy materials in a much more efficient way. Toward materials design for future technologies, a fundamental understanding of the process/structure/property/performance linkage on an atomistic level will promote prescreening and selection of material candidates. With the help of computational material science, in which the fast development of computational capabilities that have a rapidly growing impact on new materials design, this fundamental understanding can be approached. Furthermore, high-throughput experimental methods, enabled by the advances in instrumentation and electronics, will accelerate the production of large quantities of results and stimulate the identification of the target products, adding knowledge in computational design. This review shows that combining computational preselection and verification by high-throughput can be an efficient approach to unveil the complexities of HEMs and design novel HEMs with enhanced properties for energy-related applications

ASCR/HEP Exascale Requirements Review Report

This draft report summarizes and details the findings, results, and recommendations derived from the ASCR/HEP Exascale Requirements Review meeting held in June, 2015. The main conclusions are as follows. 1) Larger, more capable computing and data facilities are needed to support HEP science goals in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of the demand at the 2025 timescale is at least two orders of magnitude -- and in some cases greater -- than that available currently. 2) The growth rate of data produced by simulations is overwhelming the current ability, of both facilities and researchers, to store and analyze it. Additional resources and new techniques for data analysis are urgently needed. 3) Data rates and volumes from HEP experimental facilities are also straining the ability to store and analyze large and complex data volumes. Appropriately configured leadership-class facilities can play a transformational role in enabling scientific discovery from these datasets. 4) A close integration of HPC simulation and data analysis will aid greatly in interpreting results from HEP experiments. Such an integration will minimize data movement and facilitate interdependent workflows. 5) Long-range planning between HEP and ASCR will be required to meet HEP's research needs. To best use ASCR HPC resources the experimental HEP program needs a) an established long-term plan for access to ASCR computational and data resources, b) an ability to map workflows onto HPC resources, c) the ability for ASCR facilities to accommodate workflows run by collaborations that can have thousands of individual members, d) to transition codes to the next-generation HPC platforms that will be available at ASCR facilities, e) to build up and train a workforce capable of developing and using simulations and analysis to support HEP scientific research on next-generation systems.Comment: 77 pages, 13 Figures; draft report, subject to further revisio

Methods to Monitor and Simulate Existing Residences: Analyzing and Improving Energy and Comfort for Native Hawaiian Homeowners

To support the State of Hawaii’s goals of improving energy performance of buildings and reducing dependence on fossil fuels, this study develops design recommendations that could improve the energy efficiency and thermal comfort of hundreds of existing and future homes for native Hawaiian families. This poster shares the methods and learning objectives used by faculty, researchers, and a team of architecture, electrical and mechanical engineering, and computer science students to chart a path to net-zero site energy use in residences in sub-tropical climates by monitoring and simulating existing houses. The faculty from Architecture and Sea Grant structured the research project into multiple phases over two years: 1) Monitor Existing Buildings; 2) Calibrate Simulated Whole Building Energy Models; 3) Simulate Potential Design for Future Energy Code; 4) Simulate Potential Energy Efficiency Improvements; 5) Estimate Potential Renewable Energy Production and; 6) Communicate Recommendations to Developers and Residents. In this study, three existing house typologies are studied: naturally ventilated (no air conditioning); partially air-conditioned; and centrally air-conditioned. Student and senior researcher teams monitor and manage data for temperature, humidity, and sub-metered for electricity in nine houses for one year. Air conditioning comprises a larger portion of the monitored houses’ total energy use as compared to national hot-humid climate residential averages. The monitored data shows most occupants chose to use air conditioning year round despite the mild climate and high electricity rates. In addition, monitored data shows plug loads vary between houses by more than a factor of two. Student researchers using computational building performance simulation in NREL’s BEopt with Energy Plus find that the most effective energy efficiency measures are 1) improving the air conditioning SEER rating and 2) increasing the thermostat cooling setpoint while using ceiling fans with occupancy sensors. The team graphically communicates recommendations and presents them to developers and homeowners for potential incorporation into the next hundreds of homes planned for construction

Enhancing Energy Production with Exascale HPC Methods

High Performance Computing (HPC) resources have become the key actor for achieving more ambitious challenges in many disciplines. In this step beyond, an explosion on the available parallelism and the use of special purpose processors are crucial. With such a goal, the HPC4E project applies new exascale HPC techniques to energy industry simulations, customizing them if necessary, and going beyond the state-of-the-art in the required HPC exascale simulations for different energy sources. In this paper, a general overview of these methods is presented as well as some specific preliminary results.The research leading to these results has received funding from the European Union's Horizon 2020 Programme (2014-2020) under the HPC4E Project (www.hpc4e.eu), grant agreement n° 689772, the Spanish Ministry of Economy and Competitiveness under the CODEC2 project (TIN2015-63562-R), and from the Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP). Computer time on Endeavour cluster is provided by the Intel Corporation, which enabled us to obtain the presented experimental results in uncertainty quantification in seismic imagingPostprint (author's final draft

CFD-based process optimization of a dissolved air flotation system for drinking water production

Dissolved air flotation (DAF) has received more attention recently as a separation technique in both drinking water as well as wastewater treatment. However, the process as well as the preceding flocculation step is complex and not completely understood. Given the multiphase nature of the process, fluid dynamics studies are important to understand and optimize the DAF system in terms of operation and design. The present study is intended towards a comprehensive computational analysis for design optimization of the treatment plant in Kluizen, Belgium. Setting up the modelling framework involving the multiphase flow problem is briefly discussed. 3D numerical simulations on a scaled down model of the DAF design were analysed. The flow features give better confidence, but the flocs escape through the outlet still prevails which is averse to the system performance. In order to improve the performance and ease of maintenance, design modifications have been proposed by using a perforated tube for water extraction and are found to be satisfactory. The discussion is further reinforced through validating the numerical model against the experimental findings for stratified flow conditions